The evolution of the web to a semantic web is gaining momentum. Resource Description Framework (RDF) is being widely adopted as a standard to capture the semantics of data. Facts represented as RDF (subject, predicate, object) triples can capture both relationships between resources as well as attribute values associated with a resource. A unique challenge of semantic data stores is the ability to automatically derive additional facts based on facts already asserted in the semantic model. These additional facts are derived using inference rules that model the knowledge contained in the existing facts in a process called entailment. With large semantic data models that include many asserted and inferred facts, updating the data model based on new facts by applying the inference rules to the facts in the data model including the new facts becomes a challenge in terms of performance. For example, the addition of 1000 new triples to the LUBM8000 ontology, a benchmark that includes more than a billion triples, may result in 12 hours of processing time spent firing inference rules to derive newly inferred triples that result from the addition of the newly added triples.